Electronics and information technologies / Електроніка та інформаційні технології
ISSN 2224-087Х (Print) ISSN 2224-0888 (Online) ISSN Center responsible for the record: CIEPS — ISSN Digital identifier DOI: https://doi.org/10.30970/eli Certificate of state registration of the print media: Decision of the National Council of Ukraine on Television and Radio Broadcasting No. 1877 dated May 30, 2024. Media identifier R30-04912. Language of publication: English or Ukrainian, with Abstracts in both Ukrainian and English. The collection of scientific papers "Electronics and Information Technologies" has been published since 1966. In 1966-2010, the journal was published under the title "Theoretical Electrical Engineering". "Theoretical Electrical Engineering" is one of the first scientific and technical publications in Ukraine devoted to the problems of theoretical electrical engineering, electronics, and modeling. The collection was created on the initiative of the rector of Lviv State University named after I. Franko, Professor M. Maksymovych, who was the editor-in-chief of the collection until 1981. Subsequently, the chief editors of the collection were: Professor L. Synytsky (1981-1991), Professor P. Stakhiv (1991-1996), and Professor I. Bolesta (1996-2024). Since 2025, the annual collection has been headed by chief co-editors Professors Oleh Bordun and Ivan Karbovnyk. During all the years of its existence, the journal was included in the list of publications approved by the Higher Attestation Commission, in which the main results of dissertations may be published. At present, the journal is included in the List of Scientific Professional Publications of Ukraine (Category “B”) in the “Information Technologies and Electronics” cluster in the following specialties: F2 Software Engineering; F3 Computer Science; F4 Systems Analysis and Data Science; F5 Cybersecurity and Information Protection; F6 Information Systems and Technologies; F7 Computer Engineering; G5 Electronics, Electronic Communications, Instrumentation and Radio Engineering; G6 Information and Measurement Technologies; G7 Automation, Computer-Integrated Technologies, and Robotics. All articles in the collection are assigned DOI digital identifiers from the official representative of CrossRef in Ukraine. Founder & Publisher: Ivan Franko National University of Lviv, Ukraine (EDRPOU Code: 02070987) Frequency of Publishing: Quarterly* *In some cases, one joint issue of the collection may be published at the end of the year. In that case, the articles will appear for the preview on the journal’s website as soon as they have been accepted by the editorial board. Publication Fee: Publication in the collection Електроніка та інформаційні технології / Electronics and Information Technologies is free of charge! Open Access Policy: Електроніка та інформаційні технології / Electronics and information technologies adopts a platinum open access policy to published content in accordance with the principle of free dissemination of scientific information and global exchange of knowledge for common civil progress (http://www.budapestopenaccessinitiative.org and Creative Commons Attribution 4.0 license https://creativecommons.org/licenses/by/4.0). Archiving: the full archive of the collection Електроніка та інформаційні технології / Electronics and information technologies is freely accessible without any restrictions in the National Library of Ukraine named after V. I. Vernadskyi and LOCKSS. The online archive is posted on the portal of the Ivan Franko National University of Lviv. We accept manuscript submissions for Issue 34, scheduled for publication in June 2026. Please, pay attention to the changes in the requirements for Abstracts, Materials and Methods, and References. Template Sample
We ask authors to sign an open-access license agreement. All articles are distributed under the Creative Commons Attribution (CC-BY) license (https://creativecommons.org/licenses/by/4.0), which means that readers of the collection Electronics and Information Technologies have the right to use, reproduce, publish, and distribute these articles, provided that: 1) citation with the correct indication of the authorship of the article; 2) correct entry of data on the publication and place of publishing; 3) If the original work is reproduced or distributed in part, this should be clearly indicated. Authors retain copyright and distribution rights to their works. They have the right to post and store all versions of their articles in institutional or industry repositories, including: ● preprint ● The author's manuscript was accepted by the editorial board ● the published article. The author's declaration must be signed by all authors (send a scanned version with signatures to the editorial office by e-mail).
IMAGE FUSION TECHNIQUES USING POLARIZATION INFORMATION AND INFRARED INTENSITY
Background. Fusion of polarization images with conventional thermal infrared images combines complementary physical information to improve scene interpretation. Infrared intensity imaging captures thermal emission and emissivity patterns. In contrast, polarization parameters, including the degree and angle of linear polarization, encode surface microstructure, material composition, and geometric orientation that are inaccessible to intensity-only sensing. Their fusion enhances spatial detail, target discrimination, and robustness under degraded visibility conditions, reflecting a broader shift toward multimodal perception. Materials and Methods. This study provides an analytical review of polarization–infrared fusion techniques. The development of methods is examined from classical linear transforms, multiresolution decomposition, and energy-based decision rules to adaptive hybrid frameworks. Particular attention is given to deep learning architectures and physics-informed models that incorporate polarization formation principles and radiometric constraints. The review situates fusion progress within advances in sensor technologies and computational imaging. Results and Discussion. The analysis indicates that the incorporation of polarization information substantially enhances fused image quality compared with conventional intensity-based techniques. Classical algorithms provide stable and interpretable performance but exhibit limited adaptability to complex environments. Learning-based and hybrid strategies demonstrate superior contrast preservation, structural detail enhancement, and target detectability, especially in thermally heterogeneous or visually degraded scenarios. Persistent challenges include limited annotated datasets, polarization-induced noise sensitivity, and reduced physical interpretability of deep neural representations. Conclusion. Polarization–infrared fusion represents a transition from mono-modality to multi-modal observation with the system-oriented perception integrating physical modeling with data-driven learning. Future research should emphasize physically reasoned neural design, standardized evaluation protocols, and improved interpretability to support reliable real-world deployment. Keywords: image fusion, polarization imaging, infrared imaging, multimodal data, deep learning.
Copyright (c) 2026 Oleh M. Krupych, Vitalii Artym http://creativecommons.org/licenses/by/4.0
MULTI-PARAMETER DYNAMIC LEARNING MAPS OF NEURAL NETWORKS TRAINED WITH ADAM OPTIMISER
Background. Understanding how hyperparameters of the Adam optimizer shape the learning dynamics of multilayer neural networks remains an open problem, particularly in regimes where training transitions from stable to chaotic behavior. Previous studies have shown that learning rate and network complexity can lead to underfitting, efficient learning, overfitting, or chaotic states; however, systematic visualization of these regimes in the β₁–β₂ parameter space is still lacking. Materials and Methods. Dynamic regime maps are constructed for a multilayer neural network trained on binary-encoded digit patterns. The training process is analyzed using a recurrent two-dimensional mapping based on the error evolution of a neuron and its neighbor. Periodicities in the final iterations are identified via the convergence of periodicities method, enabling classification of learning regimes from fixed points to high-order cycles and chaos. The maps are generated for varying learning rates α, training duration, network depth, and neuron count, with β₁ and β₂ scanned over the interval [0, 0.999]. Results and Discussion. The results demonstrate strong sensitivity of Adam dynamics to the joint values of β₁ and β₂. Small learning rates (α = 0.001) produce predominantly first-order periodicity, indicating underfitting. Moderate values (α = 0.1 - 0.4 ) reveal structured transitions from rapid to slower learning as β₂ increases. In contrast, large α (≥ 0.6) leads to fragmented, non-monotonic patterns associated with overfitting and chaotic dynamics. Increasing network size introduces noisy gradients, shrinking stable regions and expanding chaotic zones, whereas smaller networks yield smoother regime transitions. Conclusion. Dynamic regime mapping in the β₁–β₂ space serves as an effective diagnostic tool for identifying stable, inefficient, and chaotic learning regimes. The findings confirm the high sensitivity of Adam to hyperparameter interactions and network architecture, showing that inappropriate combinations can induce chaotic behavior even at moderate learning rates. The proposed framework provides a novel approach for analyzing optimizer dynamics and guiding hyperparameter selection in deep learning. Keywords: Adam optimizer; learning dynamics; periodicity maps; hyperparameter tuning; chaotic learning; multilayer neural networks.
Copyright (c) 2026 Serhiy Sveleba, Ivan Katerynchuk, Yaroslav Shmyhelskyy, Lucjan Pelc, Volodymyr Brygilevych, Nazar Sveleba http://creativecommons.org/licenses/by/4.0
SEMANTIC TEXT SIMILARITY ESTIMATION ACROSS DIVERSE LARGE LANGUAGE MODELS
Background. Semantic Text Similarity (STS) estimation is one of the useful proxy-based approaches for analyzing semantic differences between responses generated by various large language models (LLMs) used in AI chats and AI agents. Various studies in recent years have attempted to estimate STS for AI-generated responses, but they typically focus on ground-truth estimation by comparing AI-generated responses with predefined answers that, in most cases, differ structurally from the generated texts. However, cross-model STS estimation can provide valuable insights into model similarity, independent of how well they align with the predefined answers. Materials and Methods. In this paper, we estimate both types of STS, but primarily focus on the STS between responses generated by several selected LLMs from different providers. We build our estimation framework using a subset of Frequently Asked Questions (FAQs) in English from the MFAQ dataset, as it offers a wide range of topics and a clear, easy-to-use structure. Results and Discussion. The results of our experiments are organized into two parts. In the first part, we estimate ground-truth STS scores using multiple metrics (Cosine Similarity, BERTScore, and Latent Semantic Analysis (LSA)) to analyze how closely the generated answers from each model match the ground-truth answer in the MFAQ dataset. In the second part, we estimated cross-model STS scores using the same metrics to analyze how closely models align with each other. Additionally, we measure average request latency and use publicly available information on model context size and input and output prices to explore possible relationships between these parameters and STS-based quality indicators. Conclusion. Our results indicate relatively high semantic similarity between models from the same provider in the selected experimental setup and demonstrate that high ground-truth STS scores do not necessarily imply high inter-model semantic similarity within the selected benchmark and metrics, as seen in the analysis of cross-model STS scores. Additionally, we show that not all metrics are equally suitable for cross-model STS estimation, and that metrics like Cosine Similarity or LSA scores can provide better sensitivity than BERTScore or similar metrics. Keywords: semantic text similarity (STS), large language model (LLM), MFAQ dataset, cosine similarity, BERTS core, latent semantic analysis (LSA)
Copyright (c) 2026 Roman Shuvar, Vitalii Parubochyi, Oleksandr Chmykhalo, Oleh Husak http://creativecommons.org/licenses/by/4.0
CLOUD-AGNOSTIC INTELLIGENT MONITORING ARCHITECTURE FOR CLIMATE ANOMALIES IN EARLY WARNING SYSTEMS
Background. Climate-related hazards require early warning systems to combine heterogeneous environmental observations, detect anomalous spatiotemporal patterns, and disseminate warning signals to stakeholders. Existing solutions separate software architecture, anomaly-detection models, and decision logic to transform deviations into early signals. Materials and Methods. The study develops a cloud-agnostic intelligent monitoring architecture for early warning systems, supported by ML-based anomaly identification and decision support. A hierarchical anomaly model is introduced for climate zones, areas, and regions. It evaluates four types of evidence: deviation from normal state, consistency with neighboring regions, consistency with parent area, and the influence of contextual factors. The method combines region-specific detector training, multi-component anomaly scoring, persistence-based warning confirmation, and CEP-based escalation. A synthetic spatiotemporal climate graph was used to compare the Robust Z-score statistical method, the Isolation Forest anomaly-detection algorithm, the One-Class Support Vector Machine method, and the proposed HCOIM-EWS method, where HCOIM-EWS denotes Hierarchical Context-Orchestrated Intelligent Monitoring for Early Warning Systems. Results and Discussion. The proposed architecture integrates ML-based detector selection, region-specific training, hierarchical anomaly scoring, CEP-based warning logic, and notification distribution. In simulation, HCOIM-EWS achieved the lowest false-alarm count while maintaining comparable event-level recall. Compared with Robust Z-score, Isolation Forest, and One-Class SVM, the proposed method reduced false alarms by 55.0%, 22.6%, and 49.7%. The method is suitable for warning-oriented intelligent monitoring, where operational reliability and false-alarm reduction are as important as point-level sensitivity. Conclusion. The proposed method extends anomaly detection in early warning systems from isolated local time-series analysis to hierarchical, context-aware, and event-driven intelligent monitoring. This confirms the technical feasibility of the architecture and justifies its use as a reusable template for climate-risk monitoring platforms. Keywords: anomaly detection, early warning systems, intelligent monitoring, cloud-agnostic architecture, complex event processing, machine learning
Copyright (c) 2026 Vasyl Lyashkevych, Yurii Marchuk, Petro Kulyk, Andrii Patynko, Taras Kerod http://creativecommons.org/licenses/by/4.0
CONVOLUTIONAL NEURAL NETWORKS AND GRADIENT BOOSTING COMPARATIVE ANALYSIS FOR MULTI-HORIZON CLASSIFICATION OF CRYPTOCURRENCY STREAM DATA
Introduction. Cryptocurrency price direction classification is a binary classification task for time series with low composite signal/sum. Image encoding methods (GASF, MTF, Recurrence Plot) transform one-dimensional time series into two-dimensional images for processing by CNNs. However, a systematic comparison of the performance of such approaches with classical sequential and tabular methods for different data scales and forecasting horizons is lacking. Materials and methods. A streaming pipeline based on Apache Kafka is proposed for collecting and processing OHLCV data of cryptocurrency trading pairs from the Binance exchange. Three series of experiments of different scales are conducted: small (46 pairs, 3 months, 10 948 samples), medium (91 pairs, 6 months, 46 355) and large (147 pairs, 12 months, 139 887). For each scale, six CNN architectures are compared – five with 2D image coding (SimpleCNN, HybridCNN, HybridResCNN, EfficientNet-B0, MobileNetV2) and one 1D control (CNN1D-Hybrid) – with gradient boosting (LightGBM) on tabular features. Classification was performed for four forecast horizons (1, 3, 6, and 12 intervals of 8 hours). Statistical significance was assessed using the McNemar criterion. Results and Discussion. At a small scale, the 1D control model achieves the highest Matthews correlation coefficient (MCC) = +0.21 at horizon h = 1, while the best 2D image-based result is only MCC = +0.11. At medium scale, all CNN models exhibit MCC ≈ 0, while LightGBM consistently dominates with MCC up to +0.09 at horizons h = 6 and h = 12. At large scale, no model exceeds MCC = +0.07, indicating saturation of the predictive signal with increasing data heterogeneity. 2D image-based coding does not provide a consistent advantage over 1D sequential and tabular approaches at any scale. Conclusion. The transformation of time series into two-dimensional images for convolutional neural networks does not provide a sustainable advantage over tabular gradient boosting and 1D convolutional networks in classifying cryptocurrency price direction. Naive scaling by increasing the number of trading pairs and time window worsens results of all model classes due to increasing heterogeneity of market regimes. These results indicate a need for regime-adaptive and cluster approaches instead of building a single global model. Keywords: data classification; time series imaging; convolutional neural networks; gradient boosting; data streaming; Apache Kafka.
Copyright (c) 2026 Ivan Khamar http://creativecommons.org/licenses/by/4.0
EVOLUTION OF IMAGE SEGMENTATION METHODS: CLASSICAL ALGORITHMS AND DEEP LEARNING
Background. Text image binarization is an important preprocessing task in document analysis, optical character recognition, and automated information extraction. The task becomes challenging when images contain blur, noise, low contrast, or background degradation. Materials and Methods. This study compares classical and lightweight neural methods for binarizing degraded text images. The evaluation was conducted on a paired text binarization dataset comprising degraded input images and their corresponding clean binary target masks. The evaluated methods included simple global thresholding, Otsu binarization, adaptive thresholding, K-means clustering, Fuzzy C-means clustering, watershed segmentation, region growing, region splitting and merging, Canny edge detection, Laplacian of Gaussian filtering, and a U-Net model. Tunable parameters were selected on the validation subset, while final results were reported on the independent test subset. The methods were assessed using Intersection over Union (IoU, Jaccard index), Dice similarity coefficient (DSC), Pixel Accuracy (PA), Precision, Recall, and execution time. Results and Discussion. Adaptive thresholding achieved the best performance among classical methods: IoU = 0.785, DSC = 0.877, PA = 0.961, Precision = 0.851, and Recall = 0.923. Simple global thresholding was the fastest method, with a runtime of 0.0015 ms/image, but lower Precision. K-means, Fuzzy C-means, Otsu, and region growing demonstrated high Recall values above 0.97, but lower Precision, indicating that degraded background regions were often classified as foreground. Edge-based methods produced lower IoU and DSC because they mainly detected character contours. The Tiny U-Net model achieved the highest overall quality: IoU = 0.959, DSC = 0.979, PA = 0.994, Precision = 0.979, and Recall = 0.979. Conclusion. The results show that different binarization methods are suitable for different scenarios, depending on the required accuracy, runtime constraints, and available resources. Classical methods remain useful for low-latency processing, while lightweight neural models provide higher accuracy when sufficient computational resources are available. Pixel Accuracy should be interpreted carefully because background pixels dominate document images. Keywords: text binarization, image segmentation, thresholding, clustering, U-Net, evaluation metrics.
Copyright (c) 2026 Viktor Maryanovich Vdovychenko http://creativecommons.org/licenses/by/4.0
DEEP LEARNING ARCHITECTURES FOR VISUAL RECOGNITION OF SELECTED TYPES OF EXPLOSIVE ORDNANCE: COMPARATIVE ANALYSIS AND SYSTEM IMPLEMENTATION
Background. In the context of significant contamination of Ukraine's territory with explosive ordnance (EO) resulting from the Russian Federation's aggression and over 11 years of ongoing hostilities, coupled with limited resources for detection and demining, the implementation of innovative technologies for identifying and recognizing such objects is critical. Therefore, the application of artificial intelligence-based methods for the detection and recognition of explosive devices is both relevant and essential. Materials and Methods. This article explores the potential of Deep Neural Networks (DNNs) and computer vision for automating EO detection. The study reviews the theoretical foundations and practical applications of Convolutional Neural Networks (CNNs) in object recognition tasks. The primary focus is on utilizing modern deep learning methods to identify explosive devices, specifically technologies employing single-stage (SSD – Single Shot MultiBox Detector, and YOLO – You Only Look Once) and two-stage (R-CNN, Regions-based Convolutional Neural Networks) approaches for object detection and identification. Key aspects of using single- and two-stage DNN architectures for visual object recognition are investigated, comparing the strengths and weaknesses of both approaches and examining ways to improve them to enhance detection efficiency. Results and Discussion. Based on the conducted analysis, models were developed using single-stage (SSD and YOLOv8) and two-stage (Faster R-CNN) object identification technologies. These models were trained on an original dataset created from internet-sourced images, which included eight common types of EO found in combat zones and de-occupied territories. Testing results of the trained models demonstrated the superiority of the YOLOv8 architecture for explosive device detection and recognition, which was subsequently used to develop a web server for explosive ordnance recognition. Conclusions. A project was developed to train EO recognition model architectures based on two-stage and single-stage approaches. Using these models, a web server with a REST API (Representational State Transfer Application Programming Interface) was created for EO recognition in both static images and real-time video streams. The proposed system can facilitate demining operations and reduce risks to civilian populations. Keywords: deep learning, visual recognition, explosive ordnance, SSD (Single Shot MultiBox Detector), YOLOv8, Faster R-CNN
Copyright (c) 2026 Volodymyr Hrabovskyi, Oleksii Hryhorashyk http://creativecommons.org/licenses/by/4.0
ENSEMBLE MACHINE LEARNING TECHNIQUES FOR GENOMIC VARIANT PATHOGENICITY PREDICTION
Background. The rapid development of next-generation sequencing (NGS) technologies has resulted in a massive accumulation of genomic data, creating new opportunities and challenges for identifying genetic variants associated with diseases. Traditional statistical and rule-based approaches often fail to capture the complex, non-linear relationships between genomic features and variant pathogenicity. The integration of ensemble machine learning techniques provides a promising pathway to enhance prediction accuracy and interpretability in genomic research. This study focuses on developing an ensemble-based framework for the classification of genetic variants using gradient boosting. Materials and Methods. The dataset was obtained from the open-access ClinVar repository, containing annotated genomic variants with clinical significance labels. After preprocessing and feature engineering, a set of ensemble algorithms, including HistGradientBoosting, XGBoost, and LightGBM, was trained and evaluated. The implemented pipeline was designed to be reproducible and computationally efficient, enabling model training on standard workstation environments. Results and Discussion. The conducted experiments demonstrated the effectiveness of the proposed framework for the classification of pathogenic and benign genetic variants. The results indicate that the integration of biologically informed genomic features contributes to improved predictive accuracy, robustness, and generalization across heterogeneous genomic datasets. The combination of an expanded feature space with gradient boosting ensemble algorithms achieved strong ROC-AUC and PR-AUC performance on an independent test. Conclusion. Ensemble learning represents an effective and interpretable approach for genomic variant classification and pathogenicity prediction. The study demonstrated that the integration of biologically informed feature engineering with gradient boosting ensemble models improves predictive robustness, generalization, and resilience to class imbalance in heterogeneous genomic datasets. Future work will focus on advanced deep learning components and further optimization strategies to enhance predictive performance on large-scale genomic datasets. Keywords: ensemble learning, genomic prediction, pathogenic variants, gradient boosting, feature engineering, bioinformatics.
Copyright (c) 2026 Andrii Smoliak, Ivan Kuno http://creativecommons.org/licenses/by/4.0
ADAPTIVE PROTOCOL SELECTION LAYER FOR CONTEXT-AWARE MULTI-PROTOCOL DATA DELIVERY IN CLOUD–EDGE SYSTEMS: A CONTEXTUAL-BANDIT FOUNDATION WITH BAYESIAN WARM-START
Background. Protocol performance in cloud–edge systems is regime-dependent: the transport that minimizes latency, loss, or CPU cost changes with payload, rate, link type, and signal quality, and tail latency often inverts the median ranking. Static choices and hand-tuned heuristic switchers cannot track these shifts, and many nearby adaptive-selection prototypes remain deterministic scorers, not online learners. This paper establishes the theoretical foundation for an adaptive protocol selection layer that treats protocol choice at decision ticks as online decision-making under partial feedback, demonstrated on isolated proof-of-concept examples. Materials and Methods. Protocol selection is formulated as a contextual multi-armed bandit over the five supported implementation transports and solved with LinUCB: a per-arm ridge-regression estimator with an upper-confidence-bound exploration term, computed by Gaussian elimination. The four-dimensional context couples a log-linear payload feature, normalized Wi-Fi signal and link-rate features, and a bias term. Bounded penalty and peer-relative rewards are derived from blended median/tail latency, goodput, and loss. Non-stationarity is handled by per-tick geometric decay, and a Bayesian warm-start encodes offline ridge priors in the initial state; normal per-tick decay may scale that prior before the first logged score. Results and Discussion. The five-protocol formulation is demonstrated on isolated Raspberry Pi 5B and Pi Zero 2 W examples; the adaptive algorithm uses five protocols (gRPC, gRPC-bidi, MQTT, CoAP, HTTP). Baseline runs confirm the best transport changes with payload, host, and objective; decision logs show LinUCB concentrating on a strong context-conditioned arm in stable regimes, while a deliberately retained cold-start miss exposes early-evidence path dependence; a Bayesian warm-start improves cold-start latency and throughput. Conclusion. The paper gives a reproducible contextual-bandit formulation with Bayesian warm-start for multi-protocol cloud–edge data delivery. It defines context, reward, and exploration sufficient for further research. Keywords: contextual bandit, LinUCB, adaptive protocol selection, cloud–edge systems, Bayesian prior, exploration-exploitation.
Copyright (c) 2026 Roman Nazarevych, Ivan Bolesta http://creativecommons.org/licenses/by/4.0
SOFTWARE ENVIRONMENT FOR IDENTIFICATION OF NONLINEAR MATHEMATICAL MODELS USING OPTIMIZATION PROCEDURES
Background. For an adequate description of complex systems, it is necessary to use significantly nonlinear mathematical models. An effective approach to the identification of such models is the use of optimization procedures based on various optimization methods. For the comprehensive implementation of these procedures, it is necessary to create software that meets modern trends in the field of computer modeling. Materials and Methods. To identify models, various optimization algorithms are used, which are developed within the framework of various optimization methods, in particular, within the framework of quasi-Newton methods or stochastic methods. The article considers the BFGS, L-BFGS, and L-BFGS-B algorithms, which belong to the first class of methods, and algorithms that implement the stochastic directed cone method, namely: the basic algorithm and its three modifications. A researcher engaged in mathematical modeling selects one of the software-implemented optimization algorithms that best corresponds to the formulated modeling problem. To do this, he must be able to evaluate the qualitative indicators of these algorithms, which are integrated in a single software environment. Results and Discussion. The software environment for the identification of mathematical models using optimization procedures, in addition to the specific features associated with identification, must comply with modern programming paradigms and their use. In view of this, it is proposed to use the Python programming environment for its creation. The structure of the software subsystem that implements the selection of the type of mathematical model, its structural and parametric identification and evaluation, as well as integration with other subsystems of the created software environment, is considered. General requirements for ensuring its functionality are formulated. The prospects for the application of cloud IT and AI are considered. Conclusion. The proposed software and architectural solutions allow creating a software environment that will significantly facilitate the process of computer-generated mathematical models of various complex systems. At the same time, it remains open for further modernization and expansion of functionality. Keywords: mathematical model, identification, optimization algorithms, software, architecture.
Copyright (c) 2026 Bohdan Melnyk http://creativecommons.org/licenses/by/4.0
ADAPTIVE SPECTRAL NOISE REDUCTION TO IMPROVE SPEECH RECOGNITION
Background. Speech recognition technology is an important component of intelligent systems in various fields, in particular for voice control of smart home devices, personal identification in security systems, automation of various technological processes, etc. Increasing requirements for accuracy, speed, and autonomy of systems require improvement of speech recognition methods, especially when affected by background noise. Therefore, the goal of this work was to develop an adaptive noise reduction method to improve the accuracy of audio information recognition. Materials and methods. The study of the impact of noise environments on the accuracy of automatic speech recognition covers the stages of spectral analysis of audio signals using short-term Fourier transformation, adaptive noise reduction using soft masks and Wiener filter elements, and signal reconstruction based on inverse Fourier transform. The study uses Whisper speech recognition models and Common Voice and LibriSpeech datasets, supplemented with synthesized noisy recordings. Results and Discussion. It has been established that various types of background noise (specifically low-frequency stationary, percussive, impulse, tonal, and broadband) and signal-to-noise ratios affect the speech recognition accuracy of Whisper models differently. A method to improve speech recognition accuracy based on adaptive noise reduction is proposed. This method provides flexible suppression of unwanted frequencies without significant speech distortion and allows for the compensation of even complex background noises. A reduction in the average consonant confusion level by 50–60% for the Whisper Tiny model and by 15–30% for the Whisper-1 model in real-time has been demonstrated. Conclusion. As a result of analyzing the speech recognition efficiency of Whisper models based on phonetic distortion metrics, transcription accuracy, consonant confusion, and artifact perception across a wide range of signal-to-noise ratios, it has been established that the application of adaptive noise reduction provides a significant improvement in accuracy. Despite the higher accuracy of the Whisper-1 model, the Whisper Tiny model with a soft mask demonstrates sufficient efficiency for intelligent systems based on real-time IoT platforms. Keywords: Whisper models, background noise, noise reduction, Wiener filter, spectral masking, IoT.
Copyright (c) 2026 Oleh Osadchuk, Igor Olenych http://creativecommons.org/licenses/by/4.0
METHODS AND MODELS FOR ENSURING QUALITY OF EXPERIENCE IN INFORMATION AND COMMUNICATION SYSTEMS
Background. Quality of Experience (QoE) has emerged as a fundamental metric for evaluating the effectiveness of modern information and communication systems, especially in video conferencing environments where real-time interaction is critical. Unlike traditional Quality of Service (QoS) metrics, QoE reflects the subjective perception of users and depends on a combination of technical, behavioral, and contextual factors. Materials and Methods. The research is based on a comprehensive analysis of contemporary scientific approaches to QoE evaluation in telecommunication systems. A structured set of influencing factors was identified, including technical parameters (network delay, bandwidth, packet loss, noise, and distortions), user-related characteristics (perception, expectations, and interaction behavior), and contextual conditions of service usage. Mathematical modeling methods, including linear regression, neural networks, decision trees, Bayesian approaches, and similarity functions, were considered for predicting QoE. Additionally, the E-model was applied as a standardized approach for evaluating QoS. Results and Discussion. The experimental results demonstrate that network impairments have a significant negative impact on both QoS and QoE in video conferencing systems. In particular, increased network delays lead to communication interruptions and reduced interactivity, while packet loss results in audio degradation and visual artifacts. The data obtained in this work reveals a strong correlation between objective network parameters and subjective user satisfaction. The conducted modeling confirmed the existence of a nonlinear dependence of QoE on network parameters, particularly delay and packet loss. These results demonstrate the effectiveness of an integrated QoE model in evaluating network services. Conclusion. The study confirms that network delay and packet loss are among the most influential factors affecting user experience in video conferencing systems. The proposed modeling approach allows for more precise evaluation and forecasting of QoE by incorporating both objective and subjective components. The findings highlight the importance of integrating multiple parameters into unified models for accurate QoE prediction. Keywords: Quality of Experience, Quality of Service, video conferencing, model, adaptation, communication system.
Copyright (c) 2026 Petro Pelekh http://creativecommons.org/licenses/by/4.0
DUAL-STREAM MODEL OF LONG SHORT-TERM MEMORY FOR AIR QUALITY PREDICTION USING STATION AND WEATHER DATA
Background. Air pollution remains a critical environmental and public health issue, requiring accurate and reliable forecasting methods to support decision-making and mitigation strategies. With the increasing availability of environmental data from monitoring stations and meteorological sources, advanced data-driven approaches have become essential for modeling complex air quality dynamics. The development of robust deep learning architectures capable of capturing both temporal dependencies and external environmental influences is of significant importance. Methods. This study proposes a Dual-Stream Long Short-Term Memory (LSTM) model for air quality forecasting, which processes station-based and meteorological data through two parallel streams. A preprocessing pipeline is applied, including K-nearest neighbors (KNN) imputation for handling missing values and Z-score normalization to standardize input features. The outputs of the LSTM streams are integrated using an attention-based fusion mechanism, which adaptively assigns importance to each data source. The model performance is compared with several baseline approaches. Results and Discussion. Experimental results demonstrate that the proposed model achieves improved prediction accuracy and stability compared to traditional machine learning methods and single-factor models. The attention-based fusion mechanism effectively captures the dynamic relationships between station data and meteorological conditions, allowing the model to adapt to varying environmental influences. Evaluation using the Index of Agreement, coefficient of determination, and Root Mean Square Error confirms the superiority of the proposed approach in modeling complex air quality patterns. Conclusion. The proposed Dual-Stream LSTM framework provides an effective solution for integrating heterogeneous environmental data in air quality forecasting tasks. By combining structured preprocessing techniques with an adaptive fusion strategy, the model enhances prediction performance and robustness. The results highlight the potential of multi-source deep learning approaches for environmental monitoring and can be extended to other time-series forecasting applications. Keywords: air quality prediction; LSTM; dual-stream model; KNN imputation; Z-score; sensor.
Copyright (c) 2026 Ivan Rudavskyi, Halyna Klym http://creativecommons.org/licenses/by/4.0
SPEECH SEPARATION BY MODIFIED DEEP NON-LINEAR FILTERING MODEL
Background. Automatic Speech Recognition (ASR) systems in single-channel experimental setups perform poorly in real-world scenarios when several talkers are placed near a microphone. While for multi-channel setups, speech (or source) separation can be solved by algorithmic approaches such as beamforming or machine learning approaches using deep non-linear filtering models, there is no such monaural algorithm that can efficiently separate speech. Materials and Methods. We focus on a successive model designed for multi-channel speech extraction with a steering mechanism called deep non-linear filtering and modify it for single-channel speech separation. This LSTM-based (LSTM - Long short-term memory) model without a steering mechanism demonstrates high-quality speech extraction from a fixed angle, which makes the model promising for single-channel separation. We modified such a model by appending a parallel output head that generates a second gain mask in addition to the existing one. We train the model on the Libri2Mix dataset in noisy conditions to separate speeches and extract them without background noise. Additionally, we investigate the influence of bidirectional LSTM layers versus the unidirectional version. As the DNLF (Deep non-linear filtering) model is built with bidirectional LSTM layers, it cannot be used for real-time separation processing. Results and Discussion. Our trained DNLF-SS-UNI (Deep non-linear filtering speech separation unidirectional) model with unidirectional LSTM layers demonstrates 3.41 dB of SI-SDR (Scale-invariant signal-to-distortion ratio) and 5.34 dB of SI-SDRi (Scale-invariant signal-to-distortion ratio improvement) on test subset of Libri2Mix dataset with background noise, while its bidirectional version called DNLF-SS-BI (Deep non-linear filtering speech separation bidirectional) achieves 5.82 dB and 7.76 dB for SI-SDR and SI-SDRi, respectively. However, the bidirectional version of the model requires approximately 2.3 times more parameters than its unidirectional counterpart. Conclusion. The use of bidirectional LSTM layers in speech separation models of architectures such as deep non-linear filtering is crucial, as a model with unidirectional LSTM layers instead shows a performance drop of approximately 2.4 dB for SI-SDR and SI-SDRi metrics. Keywords: recurrent neural networks, speech separation, machine learning
Copyright (c) 2026 Andrii Tsemko, Ivan Karbovnyk http://creativecommons.org/licenses/by/4.0
ELASTIC PROPERTIES OF RbNH4SO4 CRYSTALS
Background. The elastic properties of a material are of great importance in solid-state physics, as they significantly influence the optical properties of crystals. Materials and Methods. The elastic properties of rubidium ammonium sulfate (RbNH4SO4) crystals are investigated in this work. The calculation of elastic properties was carried out using the CASTEP program. Results and Discussion. Analysis of the elastic coefficients matrix of RbNH4SO4 reveals that this crystal belongs to the rhombic system with pronounced elastic anisotropy. The crystal is mechanically stable, with the greatest rigidity along the Y–direction and the smallest along the X–direction. The most stable shear is in the XZ plane, and the least stable is in the XY plane. The calculation of the Cauchy relations revealed a significant deviation (Cij > Ckl), indicating the predominance of non-central forces in interatomic interaction and a significant contribution of the covalent or ionic component of the bond in the RbNH4SO4 lattice. The spatial distributions of Young's modulus, Young's modulus under universal compression, shear modulus, and Poisson's ratio were calculated. The degree of anisotropy in the elastic properties of the RbNH4SO4 crystal was estimated, confirming its significant anisotropy. Conclusion. The Born criteria calculation showed that the crystal is mechanically stable. The calculation of the Cauchy relations revealed the predominance of non-central forces in the interatomic interaction and the presence of a significant contribution of the covalent or ionic component of the bond in the RbNH4SO4 lattice. The presence of rigid tetrahedral groups [SO]_4^2- causes the formation of covalent directed bonds within these groups. It is shown that the polarizability of ions is important: rubidium ions (Rb+) and sulfate groups [SO]_4^2- have large electron shells that deform during mechanical compression of the crystal. This leads to the formation of induced dipole moments. The complex nature of the forces in the RbNH4SO4 crystal is a consequence of the fact that the crystal consists not of point charges, but of polarizable ions and molecular complexes [SO]_4^2- with their own internal covalent bond. This makes the material elastically anisotropic and sensitive to the direction of the applied load. Keywords: crystal, anisotropy, elastic coefficients, Young's modulus, universal compression modulus, Poisson's ratio.
Copyright (c) 2026 Oleh Markevych, Vasyl Stadnyk, Oleh Bovgyra, Roman Lys, Igor Matviishyn, Oleh Onufriv http://creativecommons.org/licenses/by/4.0